STORY-TRÅD·industri

STADLERconnect turns sorting plant data into smarter maintenance and automation

A MRF can generate enormous amounts of data without necessarily telling an operator what to do with it. A change in motor load could signal a developing equipment problem. Repeated short conveyor stoppages can add up to hours of lost production. Changes in the incoming material stream can affect both throughput and sorting performance. The challenge is making sense of those signals and responding early enough to make a difference. STADLERconnect is designed to turn that plant data into actionable information. The cloud-based platform collects machine and material data from across the MRF to generate operational insights and recommended actions. "Our plants get larger and larger and then also more complex to operate," says Dr. Nils Kroell, Head of Digital Solutions at STADLER. "We want to take some stress off the operators and support them with a better overview of the plant." Cloud connectivity extends that visibility beyond the plant itself — it also brings together information that has often existed in separate systems. Connecting machine and material data for better sorting performance Machine data provides information about how equipment is operating, including machine status, motor loads, temperatures, conveyor behaviour, and downtime. Material data provides another view of plant performance, tracking the composition, quantity, and quality of material as it moves through the sorting process. Looking at either independently leaves part of the story untold. A machine's performance can affect throughput or the quality of the sorted material, while changes in the incoming material can alter how equipment behaves. Bringing the two data streams together gives operators more context for understanding not only what is happening in the plant, but what may be causing it. STADLERconnect is structured around three areas: Digital Maintenance, Material Intelligence, and Adaptive Plant Control. Together they help operators understand what happened, why it happened, and what to do next. The first of those areas, Digital Maintenance, encompasses four modules: Predictive Maintenance, the Support Portal, Downtime Tracker, and Blockage Detection. Using predictive maintenance to prevent unplanned downtime Predictive Maintenance focuses on identifying developing equipment problems before they result in unplanned downtime. STADLERconnect's Predictive Maintenance combines existing plant signals, such as motor load and energy consumption, with vibration and temperature sensors installed on critical components, including bearings, motors, and shafts. Data from the plant is collected and standardized locally through an edge device, a small industrial computer that connects the plant to the cloud platform. From there, algorithms monitor the data for anomalies and compare patterns with historical information collected from other machines. Kroell says vibration is currently one of the strongest indicators. "We can detect very small increases in the vibrations," he says. "Typically, we see things before the operators even notice." In one trial, STADLER detected an early indication of degradation and, under controlled conditions, continued monitoring the equipment as the issue developed. The machine remained operational for another six months, providing valuable insight into how the condition progressed from the first detectable signs through to eventual failure. That kind of advance warning can change how maintenance is planned. Rather than reacting when a component fails and production stops, the maintenance team can order parts and arrange service support. "What we try to do with Predictive Maintenance is basically move equipment replacement from an unplanned downtime to a planned maintenance," Kroell says. Predictive Maintenance complements scheduled maintenance. STADLERconnect's Maintenance Task Manager tracks routine work based on calendar intervals and operating hours, while predictive monitoring identifies developing conditions that may require attention between those intervals.